Modin uses Ray to provide an effortless way to speed up your pandas notebooks, scripts,
and libraries. Unlike other distributed DataFrame libraries, Modin provides seamless
integration and compatibility with existing pandas code. Even using the DataFrame
constructor is identical.

To use Modin, you do not need to know how many cores your system has and you do not need
to specify how to distribute the data. In fact, you can continue using your previous
pandas notebooks while experiencing a considerable speedup from Modin, even on a single
machine. Once you’ve changed your import statement, you’re ready to use Modin just like
you would pandas.

The modin.pandas DataFrame is an extremely light-weight parallel DataFrame. Modin
transparently distributes the data and computation so that all you need to do is
continue using the pandas API as you were before installing Modin. Unlike other parallel
DataFrame systems, Modin is an extremely light-weight, robust DataFrame. Because it is so
light-weight, Modin provides speed-ups of up to 4x on a laptop with 4 physical cores.

In pandas, you are only able to use one core at a time when you are doing computation of
any kind. With Modin, you are able to use all of the CPU cores on your machine. Even in
read_csv, we see large gains by efficiently distributing the work across your entire
machine.

We have focused heavily on bridging the solutions between DataFrames for small data
(e.g. pandas) and large data. Often data scientists require different tools for doing
the same thing on different sizes of data. The DataFrame solutions that exist for 1KB do
not scale to 1TB+, and the overheads of the solutions for 1TB+ are too costly for
datasets in the 1KB range. With Modin, because of its light-weight, robust, and scalable
nature, you get a fast DataFrame at 1KB and 1TB+.

Modin is currently under active development. Requests and contributions are welcome!